Backscatter estimation using progressive self interference cancellation

ABSTRACT

Techniques for estimating one or more backscatter signals reflected from one or more objects are disclosed. In one example, a backscatter sensor includes, in part, a receiver for receiving a composite signal comprising one or more reflections of a transmitted signal, each reflection being reflected by one of a plurality of objects; and a processor configured to estimate at least a first backscatter component of the composite signal using a progressive interference cancellation technique. The first backscatter component of the composite signal corresponds to a reflection of the transmitted signal from a first object. In one embodiment, the backscatter sensor includes multiple receivers and/or one or more transmitters.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims benefit under 35 USC 119(e) of U.S.Provisional Application No. 61/904,428, filed Nov. 14, 2013, entitled“Backscatter Estimation Using Progressive Self InterferenceCancellation,” the content of which is incorporated herein by referencein its entirety.

The present application incorporates herein by reference in its entiretythe contents of commonly assigned U.S. Application No. 61/864,492, filedAug. 9, 2013, entitled “Full Duplex Radio.”

FIELD OF THE INVENTION

The present invention relates to signal estimation, and moreparticularly to a system and method for estimating properties ofbackscatter signals using progressive self-interference cancellation.

BACKGROUND

Detecting and measuring backscatter signals present many challenges. Onechallenge is that the signal being transmitted leaks over to the radio'sreceiver and causes a large amount of self-interference, which maycompletely drown out the backscatter because of the limited dynamicrange of commercially available radios. Another challenge is that thedifferent components in the backscatter itself may act asself-interference to each other. For example, if there is a nearbyreflector, the corresponding reflection may be stronger compared toreflections from a reflector that is further away. The differencebetween the two reflections, in some cases could be as much as 50-60 dB.With strong interference from the nearby reflectors, the weakreflections may get swamped out. Therefore, there is a need in the artfor techniques to estimate parameters of backscatter signalscorresponding to different objects in the environment.

SUMMARY

In one embodiment, a backscatter sensor is disclosed. The backscattersensor includes, in part, a receiver for receiving a composite signalcomprising one or more reflections of a transmitted signal, eachreflection being reflected by one of a plurality of objects; and aprocessor configured to estimate at least a first backscatter componentof the composite signal using a progressive interference cancellationtechnique. The first backscatter component of the composite signalcorresponds to a reflection of the transmitted signal from a firstobject. In one embodiment, the first object is the closest object to thereceiver among the plurality of objects.

In one embodiment, the receiver further includes, in part, a pluralityof receive antennas, each receive antenna being coupled to a receivechain. Each receive chain receives a modified copy of the compositesignal. The backscatter sensor may further include, in part, atransmitter for transmitting the transmitted signal. The processorfurther receives a sample of the transmitted signal. In one embodiment,the transmitter includes one or more transmitters, each transmittertransmitting a signal.

In one embodiment, the processor is further configured to estimate atleast one of an amplitude of the first backscatter component, a phase ofthe first backscatter component and a time delay between transmission ofthe transmitted signal and reception of the first backscatter component.

In one embodiment, the processor is further configured to remove theestimated first backscatter component from the composite signal togenerate a second signal; and estimate a second backscatter componentusing the second signal. The second backscatter component may correspondto a reflection of the transmitted signal from a second object.

In one embodiment, the processor is further configured to estimate oneor more parameters associated with at least the first backscattercomponent using a linear optimization technique. In one embodiment, theprocessor is further configured to estimate the one or more parametersin accordance with the following expression:

$\begin{matrix}{{minimize}\;} & {\left. {\Sigma_{m}\Sigma_{n}}||{{h_{m}\lbrack n\rbrack} - {{\overset{\_}{h}}_{m}\lbrack n\rbrack}} \right.||^{2}\mspace{79mu}} \\{{subject}\mspace{14mu} {to}} & {{{\tau_{k} \geq 0},{\alpha_{k} \leq 1},}} \\\; & {{{\theta_{k} \in \left\lbrack {\frac{- \pi}{2},\frac{\pi}{2}} \right\rbrack},{\mu_{k} \in \left\lbrack {{- \pi},\pi} \right\rbrack},}\mspace{34mu}} \\\; & {{k = \left\{ {1,\ldots,L} \right\}},{n = \left\{ {{- N},\ldots,N} \right\}},} \\\; & {{m = \left\{ {1,\ldots,M} \right\}}}\end{matrix}$

where {tilde over (h)} represents an estimated channel response, Mrepresents number of antennas, L represents number of reflectedbackscatter components, α_(k) represents an attenuation undergone by thek^(th) backscatter component, ν_(k) represents a phase rotation seen bythe k^(th) backscatter component, τ_(k) represents a delay between thek^(th) backscatter component and a corresponding transmitted signal, andΘ_(k) represents an angle of arrival of kth backscatter component.

In one embodiment, the processor is further configured to estimate theat least the first backscatter component using a Sequential ConvexProgramming (SCP) algorithm. In one embodiment, the processor is furtherconfigured to generate an initial estimate for the SCP algorithm using acontinuous basis pursuit (CBP) algorithm. In one embodiment, theprocessor is further configured to estimate the at least the firstbackscatter component using a continuous basis pursuit (CBP) algorithm.

In one embodiment, the receiver is further configured to receive thecomposite signal during a plurality of consecutive non-overlapping timewindows, and the processor is further configured to estimate one or moreparameters of the first backscatter component based at least on a firstportion of the composite signal received in a first window; anditeratively estimate parameters of a next backscatter component based ona second portion of the composite signal received in a next consecutivewindow. In one embodiment, the consecutive non-overlapping windows havea variable size.

In one embodiment, the processor is further configured to estimate aDoppler frequency corresponding to each object.

In one embodiment, a method for estimating backscatter signals isdisclosed. The method includes, in part, receiving a composite signalcomprising one or more reflections of a transmitted signal, eachreflection being reflected by one of a plurality of objects; andestimating at least a first backscatter component of the compositesignal using a progressive interference cancellation technique, whereinthe first backscatter component of the composite signal corresponds to areflection of the transmitted signal from a first object.

BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of the nature and advantages of various embodiments maybe realized by reference to the following figures. In the appendedfigures, similar components or features may have the same referencelabel. Further, various components of the same type may be distinguishedby following the reference label by a dash and a second label thatdistinguishes among the similar components. If only the first referencelabel is used in the specification, the description is applicable to anyone of the similar components having the same first reference labelirrespective of the second reference label.

FIG. 1 is a simplified block diagram of a wireless backscatter system,according to an embodiment of the present invention.

FIG. 2 illustrates an exemplary backscatter sensor, according to anembodiment of the present invention.

FIG. 3 is a simplified multi-antenna system receiving two backscatterreflections, according to an embodiment of the present invention.

FIGS. 4A through 4C illustrate exemplary backscatter channels, accordingto one embodiment.

FIG. 5 illustrates an exemplary block diagram of a backscatter sensorwith range boosting, according to one embodiment of the presentinvention.

FIGS. 6A through 6D illustrate exemplary graphs illustratingintermediate results of an estimation of the backscatter signal afterperforming the progressive self-interference cancellation, according toone embodiment of the present invention.

FIG. 7 illustrates exemplary operations that may be performed by adevice to estimate backscatter signals, according to one embodiment ofthe present invention.

FIG. 8 is a simplified block diagram of an exemplary computer or dataprocessing system in which portions of self-interference cancellationcircuit may be embodied.

DETAILED DESCRIPTION

Several illustrative embodiments will now be described with respect tothe accompanying drawings, which form a part hereof. While particularembodiments, in which one or more aspects of the disclosure may beimplemented, are described below, other embodiments may be used andvarious modifications may be made without departing from the scope ofthe disclosure or the spirit of the appended claims.

Embodiments of the present invention provide a system and method forestimating properties of backscatter signals resulted from signaltransmissions from one or more transmitters. The properties for eachreflected signal may include strength of the reflected signal, the delayexperienced by the reflected signal with respect to the transmittedsignal, an angle of arrival of the reflected signal, and any otherproperties.

In one embodiment, such properties may be used by a device to detectspecific objects around the device and locate their positions. Forexample, in one embodiment, the device may estimate dielectricproperties of the reflector and infer the nature of the object based onstrength of the reflection and the relative delay from the reflector. Inanother embodiment, the device can estimate location of the reflectingobject by using the delay of the signal and angle of arrival of thesignal, In general, accuracy of the estimations can be improved bycollecting similar information from multiple antennas and combining themwith appropriate algorithms.

As used herein, a device may refer to any type of wired and/or wirelessdevice capable of transmitting and/or receiving signals. For example,the device may be a mobile device, a phone (compatible with any wirelessstandards, such as long term evolution (LTE), 3^(rd) generationpartnership project (3GPP)), a tablet, a head mount display, a Wi-Fiscanner, a radio frequency transceiver, wireless LAN device, an RFtransceiver, and any other type of devices. Moreover, an object mayrefer to any physical material that can absorb and/or reflect a portionof a signal. For example, an object may refer to walls, furniture,buildings, trees, human body, and any other material other than air,that can reflect at least a portion of a signal.

As described earlier, detecting and measuring backscatter signals maypresent many challenges, such as self-interference from thetransmissions of a signal itself, and/or self-interference caused byreflections from objects that are closer to the transceiver that maydrown out the backscatter signals reflected from other objects that arefurther away. To be able to detect all the different components in thebackscatter accurately, the self-interference signal should to beremoved from the received signal and each of the individual reflectionsneed to be measured.

Certain embodiments disclose techniques for estimating parameters ofeach reflection (e.g., amplitude, delay, angle of arrival, and the like)from a signal that contains a larger number of reflections combinedtogether into one composite signal. In general, some or all of thereflections may be closely-spaced and well within the sampling intervalof the receiver. For example, there may be two reflections that arespaced 1 ns apart (or ½ foot apart in distance). With a radio with atypical sampling rate of 40 Msps, digital samples are taken every 25 ns.In other words, the time resolution requires much smaller values thaneven what the sampling itself can provide. One option could be toincrease the sampling rate, for example to have 1 ns resolution, whichmeans using an ADC with a sampling rate of 1 GHz. However, suchhigh-rate ADCs are prohibitively expensive both in terms of cost as wellas power consumption and are infeasible for most radios.

Techniques presented herein may be used in a full-duplex transceivercoupled to one or more antennas, a half-duplex transceiver, abackscatter sensor, or any other type of system capable of measuringand/or processing backscatter signals.

FIG. 1 is a simplified block diagram of a full-duplex wirelessbackscatter estimation system 100, according to an embodiment of thepresent invention. Wireless backscatter estimation system 100 may be acommodity multiple-access transceiver system capable of supportingcommunication with multiple users by sharing the available systemresources. Examples of such wireless systems include code divisionmultiple access (CDMA) systems, time division multiple access (TDMA)systems, long term evolution (LTE) systems, orthogonal frequencydivision multiple access (OFDM) systems or the like. The wirelessbackscatter estimation system may transmit wireless signals. Thetransmitted signals will be reflected by objects that are located aroundthe wireless system and many of the reflections arrive back at thewireless system.

Wireless backscatter estimation system 100 includes, in part, adigital-to-analog converter (DAC) 102, a frequency up-converter 104, anda power amplifier (PA) 106 disposed in a transmit path 110. Transmitpath 110 may include one or more be coupled to one or more antennas 112(only one is shown in the figure). The one or more antennas may beconfigured as a multiple input multiple output (MIMO) antenna array. Inone embodiment, antenna 112 is coupled to the transmit chain through acirculator 113. A first port of the circulator receives the transmitsignal X from the power amplifier PA 106, a second port of thecirculator is coupled to the antenna 112 and a third port of thecirculator is coupled to a receive path 130.

Receive path 130 may include a low noise amplifier LNA 118, a frequencydown-converter 120, a progressive self-interference cancellation (PIC)block 122, an automatic gain control circuitry (AGC) 124, ananalog-to-digital converter (ADC) 126, and a backscatter estimator 128for estimating parameters of an associated object. In an embodiment, thebackscatter estimator may pass the estimated parameters to theprogressive self-interference cancellation block 122 to progressivelycancel components in the backscatter channel. The progressiveself-interference cancellation algorithms will be described in detailbelow. A controller 140 may be coupled to the RF cancellation circuit114 and/or PIC 122, and any other component in the circuit.

A portion of transmit signal X, i.e., a self-interference signal HX,leaks into the receive signal that is received by the receive path 130.Accordingly, system 100 also includes a cancellation circuit 114disposed before the LNA and configured to cancel the self-interference.In an embodiment, the cancellation circuit 114 may be coupled to thetransmit path through a coupler to receive a portion of the transmitsignal and generate a copy C of the self-interference signal that isthen added through an adder 132 to the received signal to cancel or atleast reduce the self-interference signal. The cancellation circuit isdescribed in detail in U.S. Application No. 61/864,492, which isincorporated herein by reference in its entirety.

Backscatter Sensor's Operation

FIG. 2 illustrates an exemplary backscatter sensor 202, according to oneembodiment. As illustrated, the backscatter sensor 202 uses a wirelesstransmitter 204 as a light source. The wireless signal is reflected fromdifferent reflectors in the environment and arrives at one or morereceivers (e.g., receivers 208 ₁ through 208 ₄) at different angles ofarrival. It should be noted that although four receivers are shown inthis example, the backscatter sensor may include any number of antennas,without departing from the teachings of the present disclosure.

The received signal is sampled by the receivers (e.g., Rx1 through Rx4)and the digital samples are passed to the backscatter sensor 202. Thebackscatter sensor estimates parameters of each backscatter signalimpinging on the antennas. The backscatter sensor is described in moredetail in the rest of this document.

FIG. 3 is a simplified multi-antenna transceiver 302 receiving twobackscatter reflections at different delays, angles of arrival, andamplitudes, according to an embodiment of the present invention. Asshown, the multi-antenna transceiver receives a strong reflection from afirst reflector 304, and a weak reflection from a second reflector 306.The reflections are received with amplitudes α₁ and α₂, and delays τ₁and τ₂, respectively. In other words, if a signal x(t) is transmitted,the backscatter signal is given by α₁x(t−τ₁)+α₂x(t−τ₂). The exampleshown in FIG. 3 is a simple delayed and attenuated reflection whoseimpulse response is given by α₁δ(t−τ₁)+α₂δ(t−τ₂). Note that in practice,reflections get more attenuated as delay increases because the signalwith larger delay has travelled a longer distance.

Single Receiver Signal Model

In one embodiment, a simplified model of the backscatter estimationsystem can be written as follows. The wireless transceiver istransmitting a signal x(t). There are L backscatter reflections of thetransmitted signal arriving back at the transceiver. The parameters ofthe backscatter reflections may be written as follows:

(α_(l),ν_(l),τ_(l),Θ_(l)), . . . ,(α_(L),ν_(L),τ_(L),Θ_(L))

where α_(k) represents an attenuation undergone by the k^(th)backscatter component, ν_(k) represents a phase rotation seen by thesignal, τ_(k) represents a delay between the backscatter signal and theoriginal signal, and Θ_(k) represents an angle of arrival. For ease ofexplanation, first a mathematical model of a simplified system with onlyone receiving antenna and without the use of AoA parameter is described.

The composite signal that arrives at the receiving antenna can bewritten as follows:

$\begin{matrix}{{{y(t)} = {\sum\limits_{k = 1}^{L}\; {\alpha_{k}^{{iv}_{k}}{x\left( {t - \tau_{k}} \right)}}}},} & (1)\end{matrix}$

where i represents an imaginary number.

In one embodiment, receive chain of the radio is tuned to the samecarrier frequency as the transmitter. The receiver may have a bandwidththat corresponds to the sampling rate of the ADC. For example, a 40 MspsADC would imply trying to receive a 40 MHz signal. The radio RX chaindown-converts the received signal to analog baseband, applies a low-passfilter of bandwidth B, and then digitizes the signal. In one example,the bandwidth B corresponds to half of sampling rate of the ADC. Ingeneral, the process of filtering to a particular bandwidth B can bemodeled as convolution with a filter of bandwidth B.

Since, a time domain response of a rectangular filter of bandwidth B isa sinc pulse, the overall signal received at baseband y_(B)(t) may bewritten as follows:

$\begin{matrix}{{y_{B}(t)} = {\left( {\sum\limits_{k = 1}^{L}\; {\alpha_{k}^{{iv}_{k}}{x\left( {t - \tau_{k}} \right)}}} \right) \otimes {{{sinc}\left( {B(t)} \right)}.}}} & (2)\end{matrix}$

This baseband analog signal is sampled by the ADC to produce thefollowing discrete time signal:

$\begin{matrix}{{{y_{B}\lbrack n\rbrack} = {\left( {\sum\limits_{k = 1}^{L}\; {\alpha_{k}^{{iv}_{k}}{x\left( {{nT}_{s} - \tau_{k}} \right)}}} \right) \otimes {{sinc}\left( {B\left( {nT}_{s} \right)} \right)}}},} & (3)\end{matrix}$

where T_(S) is the sampling interval.

This equation can be rearranged as follows:

$\begin{matrix}{\begin{matrix}{{y_{B}\lbrack n\rbrack} = {\left( {\sum\limits_{k = 1}^{L}\; {\alpha_{k}^{{iv}_{k}}{{sinc}\left( {B\left( {{nT}_{s} - \tau_{k}} \right)} \right)}}} \right) \otimes {x\lbrack n\rbrack}}} \\{= {{h\lbrack n\rbrack} \otimes {x\lbrack n\rbrack}}}\end{matrix},} & (4)\end{matrix}$

where the term

${h\lbrack n\rbrack}\overset{\Delta}{=}{\sum\limits_{k = 1}^{L}\; {\alpha_{k}^{{iv}_{k}}{{sinc}\left( {B\left( {{nT}_{s} - \tau_{k}} \right)} \right)}}}$

is the backscatter channel through which the transmitted signal passesthrough before reaching the receiver. In general, the backscatterchannel includes contribution from the environmental channel. Thefilters at the transmit and/or receive chains can be viewed as a channelwith impulse response sinc(B(t)). The environmental channel can beviewed as sum of impulses. Hence, the total channel through which thetransmitted signal passes may be written as sum of weighted and shiftedsinc signals, as illustrated in FIG. 4B. In the above equation, thetransmitted signal x[n] and the received signal y_(B)[n] are known.Therefore, the overall channel response h[n] can be calculated usingknown de-convolution techniques.

FIGS. 4A through 4C illustrate a backscatter channel, according to oneembodiment. FIG. 4A illustrates an exemplary RF backscatter channelcomposed of three impulses with amplitudes α₁, α₂, and α₃, and delaysτ₁, τ₂, and τ₃, respectively. As illustrated, the original reflectionsfrom the environment are closely located impulses. After going throughthe band-limited receiver, the impulses are converted to sinc functions,and the overall response is a combination of all the sinc functions.FIG. 4B illustrates channel responses corresponding to each of theindividual impulses shown in FIG. 4A. FIG. 4C illustrates the combinedbackscatter channel response that is observed. The overall responseslooks similar to the sinc function corresponding to the strongestreflection. This is why two closely-spaced reflections are difficult todisentangle. Although α_(k) and τ_(k) for k=1,2,3 need to be estimated,only the combined channel response (shown in FIG. 4C is known).

It should be noted that the combined channel response is similar to thescenario where there is only a single reflection at τ1. This is becausethe second reflection at τ2 is closely spaced in time and is also veryweak compared to the first reflection, hence, the second reflection doesnot contribute significantly to the overall response. In one embodiment,techniques are disclosed for accurately estimating individual responsesat τ1, τ2 and τ3 from the combined response in scenarios where they areseparated in time by interval smaller than the sampling interval(sub-sampling).

It should be noted that reflection coefficients exhibit a heavy taileddistribution as shown in FIG. 4B. The strongest backscatter component isthe signal that is directly leaking through from the transmit chain tothe receive chain, the next strongest backscatter component is theclosest first reflection, and so on. Furthermore, the first few leakedstrongest components can be nearly 70-80 dB stronger than the other weakmulti-path components. A typical Wi-Fi receiver has a dynamic range of60 dB. The dynamic range may specify the highest ratio between thestrongest and the weakest signals that can be received withoutsignificant distortion of the weakest signal. Consequently, the strongbackscatter components can quite easily drown out the weaker backscattercomponents. In summary, in one embodiment, the present inventiondetermines all the attenuations α, delays τ, angle of arrivals (AoA) θof one or more of the individual backscatter components accurately fromthe sampled, band-limited backscatter signal despite the limited dynamicrange and finite bandwidth of a transceiver.

Multiple Receiver Signal Model

In one embodiment, multiple receiving antennas may be present in thesystem. In this scenario, each component of the backscatter signalundergoes additional phase shift which is a function of the angle ofarrival (AoA) of the signal at each antenna. This additional phase shiftarises due to the fact that the signal has to travel additional distancedue to the relative position of the antennas in an antenna array (e.g.,a Uniform Linear Array (ULA)). In general, receive antennas may bespaced uniformly, or with varying distances, without departing from theteachings of the present disclosure. In one example, the compositebackscatter channel as seen by the m^(th) receiver in the ULA can bemodeled as follows:

$\begin{matrix}{{{h_{m}\lbrack n\rbrack} = {\sum\limits_{k}{\alpha_{k}^{i{({v_{k} + \gamma_{mk}})}}{{sinc}\left( {B\left( {{nT}_{s} - \left( {\tau_{k} + \frac{\gamma_{mk}}{2\pi \; f_{c}}} \right)} \right)} \right)}}}},} & (5)\end{matrix}$

where

$\gamma_{mk} = {\frac{2\pi}{\lambda}\left( {m - 1} \right){dsin}\; \Theta_{k}}$

is the added phase shift experienced by the k^(th) reflection at them^(th) receiver relative to the first receiver, when the reflectedsignal arrives back at Θ_(k) AoA; α_(k)e^(iν)k is the complexattenuation for the k^(th) reflection; and τ_(k) is its correspondingdelay. The constant f_(c) is the carrier frequency with wavelength of λ,and d is the distance between the successive receiving antennas in thearray. Theoretically, h_(m)[n] can be of infinite length, but inpractice, the sinc function decays to very small values for large valuesof n. Thus, the channel can be modeled by a finite length vector with ntaking a value in the range of [−N,N]. Similar to the single antennacase, the composite finite-length linear channel h_(m)[n] can beestimated by de-convolving the received signal with known transmittedsignal.

In one embodiment, one or more Doppler frequencies corresponding to eachof the reflecting objects are estimated. In one embodiment, anadditional multiplicative term may be added to Eqn (5) to estimateDoppler frequencies corresponding to each reflector. This additionalterm may be carried over in the subsequent estimation stages as anadditional variable, and be solved alongside the other variables thatare currently described. For example, for estimating Doppler frequencythe additional multiplicative term

^(i 2π f_(d_(k))t)

may be added to Eqn. (5), where f_(d) _(k) is the unknown Dopplerfrequency due to the k^(th) reflector at time instance ‘t’. For ease ofcomputation, the additional phase shift introduced by the Doppler Shiftcan be incorporated into the phase term γ_(k). In this case, the Dopplerfrequency can be estimated as the slope of phase vs the time instance‘t’ by fitting the estimated phase at different time instances ‘t’against the time instances. Another method is to introduce a newvariable f_(d) _(k) for the Doppler frequency. Then all the subsequentoptimization formulations, as described above, utilize the updated formof Eqn. (5) and solve for f_(d) _(k) along with other variables atseveral estimation time instances ‘t’. In this method, the phase isfirst estimated once without the variable f_(d) _(k) , then in thesubsequent estimation time instances, the values of the phase γ_(k)estimated in the earlier time instance is substituted in the modifiedEqn (5) and f_(d) _(k) term is estimated by adding it as a variable.Once the Doppler frequency is estimated, the corresponding velocity ofthe reflector can be inferred using standard physical relationship:ν_(k)=f_(d) _(k) C/f_(c), where f_(c) is the center frequency or thecarrier frequency, and C is the speed of the radio wave in air.

Enhanced Temporal and Spatial Resolution Algorithm

As noted earlier, wireless signal propagation in a typical indoorenvironment can easily lead to backscatter reflections that are closerthan the sampling interval of the receiving radios. In general, suchclosely-spaced reflections lead to a degenerate system of equations. Forexample, when the reflections at two delays τ₁ and τ₂ are closelyspaced, the difference in sample values of the corresponding sincfunctions are very small and therefore the observations are highlycorrelated. As more reflections are closely spaced within a samplingperiod, the problem of uniquely reconstituting the reflections becomesmore difficult. Consequently, reconstruction error increases since theoptimization algorithm struggles to find a good fit.

On embodiment discloses a technique to accurately deconstruct signalsfrom two very closely-spaced reflectors in time (i.e., the relativedelays from the transmitter to the two reflectors are within a samplingperiod of each other). The disclosed technique utilizes the fact thatthe backscatter signals reflected from the two reflectors have differentangle of arrivals at different antennas. Therefore, spatial orientationof the reflectors relative to the transmitter is different.

In one example, a transceiver has four antennas to receive differentcopies of the reflected signal. A first reflection and a secondreflection are closely spaced in time, which are received with angles Θ₁and Θ₂, respectively. These reflections can be distinguished in thespatial dimension because they exhibit different phases at differentantennas. It should be noted the two reflections will travel differentdistances which translates to phase differences across successiveantennas. In one embodiment, this constraint is incorporated into theoptimization problem to disentangle closely spaced reflections within asingle sampling period.

In one example architecture, with 4 antennas (expected to be found inthe next generation Wi-Fi radios), up to 4 closely-spaced reflectionscan be distinguished within a single sampling period. This techniqueenables the system to get the same performance as an ADC that has fourtimes the sampling bandwidth of the system while still using relativelyinexpensive commodity radios.

Estimation Algorithm

One embodiment utilizes an estimation algorithm to estimate parametersof different backscatter signals. Once the composite linear channel isestimated, parameters of the constituent backscatter components can beestimated by solving the following optimization problem:

$\begin{matrix}{\begin{matrix}{{minimize}\;} & {\left. {\Sigma_{m}\Sigma_{n}}||{{h_{m}\lbrack n\rbrack} - {{\overset{\_}{h}}_{m}\lbrack n\rbrack}} \right.||^{2}\mspace{79mu}} \\{{subject}\mspace{14mu} {to}} & {{{\tau_{k} \geq 0},{\alpha_{k} \leq 1},}} \\\; & {{{\theta_{k} \in \left\lbrack {\frac{- \pi}{2},\frac{\pi}{2}} \right\rbrack},{\mu_{k} \in \left\lbrack {{- \pi},\pi} \right\rbrack},}\mspace{34mu}} \\\; & {{k = \left\{ {1,\ldots,L} \right\}},{n = \left\{ {{- N},\ldots,N} \right\}},} \\\; & {{m = \left\{ {1,\ldots,M} \right\}}}\end{matrix},} & (6)\end{matrix}$

where {tilde over (h)} is the estimated linear channel response obtainedby the deconvolution of received samples with a known signal, M is thenumber of antennas in the array, and L is the total number of reflectedbackscatter components.

This optimization problem is non-convex and is not known to have aglobal solution. Instead, in one embodiment, the optimization problem inEqn. (6) is solved approximately by finding a locally optimal solution.For example, in one embodiment, a heuristic known as Sequential ConvexProgramming (SCP) may be used, in which all the non-convex functions arereplaced by their convex approximation. It should be noted that anyother method may be used to solve the above optimization problem withoutdeparting from the teachings of the present disclosure.

Next, the resulting convex problem is solved in a restricted domain ofvariables known as trust region to obtain a local solution. This localsolution can be improved upon by testing the goodness of theapproximated function to the original function and updating the trustregion based on the result of this test. This process can be repeated inan iterative manner until a reasonable solution is achieved.

In one embodiment, Eqn. (5) is approximated by a linear function aroundprevious solution of the convex problem in order to convert Eqn. (6)into a convex problem. It should be noted that any other method may beused to approximate Eqn. (5) and/or convert Eqn. (6) into a convexproblem without departing from the teachings of the present disclosure.

Initialization of Estimation Algorithm

As mentioned before, Eqn. (6) can be solved locally by linearizing Eqn.(5) around a point in variable domain (α,τ,Θ,ν). Therefore, theSequential Convex Programming (SCP) formulation needs a starting pointto begin its iteration. The final solution given by SCP is verydependent on the goodness of this initial estimate. One way to get agood initialization point is to try several random starting points andchoose the one that results in the smallest value for the objectivefunction of the optimization problem in Eqn. (6). However, byunderstanding the nature of the backscatter parameters, a betterinitialization point may be used, according to one embodiment.

In a typical indoor environment, there may only exist a limited numberof reflectors. Therefore, only a limited number of backscatter signal(e.g., less than five) may be present. Moreover, if energy of abackscatter signal is outside of the dynamic range of the ADC, suchreflections may not be visible to the receiver. This further reduces thenumber of backscatter signals that can be simultaneously observed by abackscatter sensor. Therefore, the solution to the optimization problemin Eqn. (6) is sparse in the variable domain (α,τ,Θ,ν).

In one embodiment, this sparsity is exploited to find a good startingpoint for the SCP formulation of Eqn. (6). As an example, continuousbasis pursuit (CBP) algorithm may be used to find the starting point.The CBP is a technique known in the art for finding sparse solutions. Ingeneral, any basis pursuit techniques (e.g., CBP, LASSO, and the like)may be used to generate a starting point for the SCP formulation withoutdeparting from the teachings of the present disclosure. In oneembodiment, the optimization problem is formulated as a LASSO problem,as follows:

$\begin{matrix}{{{\overset{.}{\beta}}_{m} = \left. \left. {\arg \mspace{14mu} \min\limits_{\beta_{m}}}||{{D\; \beta_{m}} - {\overset{\sim}{h}}_{m}}||{}_{2}{+ \lambda_{r}} \right. \middle| \beta_{m} \right|_{1}},{m = {1\mspace{14mu} \ldots \mspace{14mu} M}},} & (7)\end{matrix}$

where β_(m), m=1 . . . M are complex optimization variables.

A dictionary matrix D can be built as follows. From Eqn. (5), it isclear that the backscatter channel is sum of scaled and shifted versionof the function f_(τ)(n)=sinc(B(nTs−τ)). In general, each of the scalingfactors can be a complex number. Each column of D consists of samples offunction f_(τ)(n) for a fixed set of delays {circumflex over (τ)}₁ . . .{circumflex over (τ)}_(p). In one embodiment, the minimum delay{circumflex over (τ)}₁ and the maximum delay {circumflex over (τ)}_(p)are chosen empirically based on the expected arrival time of theearliest and the latest reflections, respectively. Next, the range ofdelay [{circumflex over (τ)}₁,{circumflex over (τ)}_(p)] is uniformlygridded with grid interval Ni. The grid interval may be selected suchthat it represents interval of the reflection arrival time that isexpected to be resolved. However, the grid intervals may be selectedusing any technique without departing from the teachings of the presentdisclosure. Note that this formulation leads to a convex problem and canbe solved accurately using any of the known methods, such as interiorpoint method for convex problems.

In one embodiment, the first term in the objective equation (7) is anerror between the computed channel {tilde over (h)}_(m) and the scaledsum of f_(τ)(n), where β_(m) has P components corresponding to the gridpoints {circumflex over (τ)}_(k). The second term in the objectiveequation, |β_(m)|₁, enforces sparsifying effect in the solution β_(m) byforcing many of the terms in the solution to be equal to zero. Here,λ_(r) represents a regularization coefficient chosen to be a positivenumber and can be chosen to tradeoff between the sparsity of solution β,and minimization of the error

$\sum\limits_{m}{\left( \left. ||{{D\; \beta_{m}} - {\overset{\sim}{h}}_{m}} \right.||^{2} \right).}$

This tradeoff can be appropriately selected by analyzing thesignal-to-noise ratio (SNR) of the signals. Once the solution β_(m) isobtained, the backscatter parameter of interest can be extracted asfollows:

${\alpha_{k} = {\sum\limits_{k}\left| \beta_{mk} \middle| {\text{/}M} \right.}},{v_{k} = {\angle\beta}_{1\; k}},{\gamma_{mk} = {{\angle\beta}_{mk} - v_{k}}},{\tau_{k} = {{\hat{\tau}}_{k} - {\frac{\gamma_{mk}}{2\pi \; f_{c}}.}}}$

In the above method of solving problem in Eqn. (7), an implicitassumption was made that the true delay τ_(k) falls on one of the gridpoints {circumflex over (τ)}₁ . . . {circumflex over (τ)}_(p) of thedictionary D. However, this may not be true in all scenarios. Therefore,in one embodiment, the problem can be modified to add interpolationfunctions so that the solution to the LASSO problem is not constrainedto fall on the chosen grid points {circumflex over (τ)}₁ . . .{circumflex over (τ)}_(p).

In one embodiment, a known technique in the art can be used to addcircular interpolation functions to the LASSO problem. The solution toEqn. (7) finds backscatter parameters for P reflections, many of whichmay have zero energy (due to the added constraint on the sparsity). Inone embodiment, L backscatter parameters with the largest energy can bechosen out of these P reflection parameters as an initial starting pointfor the SCP formulation.

Dynamic Range Boosting Algorithm

FIG. 5 illustrates an exemplary block diagram of a backscatter sensorwith range boosting, according to one embodiment. The backscatter sensorincludes a modified wireless radio that has one transmitting antenna,and up to four receiving antennas arranged in a linear configurationwith fixed distance between them. Such an antenna array configurationare commonly called Uniform Linear Array (ULA). It should be noted thatthis configuration is a mere example, and any number of transmit and/orreceive antennas with similar or different antenna spacing may be usedwithout departing from the teachings of the present disclosure.

As shown in FIG. 5, the transmitting antenna 502 is also acting as oneof the receiving antennas in the ULA. It should be noted that number ofantennas in a wireless device is usually limited by the form factor ofthe device. For example, length of a Wi-Fi antenna can be in the rangeof 12.5 cm. Therefore, sharing of antennas between transmit and receivefunctions is useful to pack as many antennas in a small form factor.However, such a sharing results in additional complexity for backscattersensor's operation which will be addressed in the following sections. Itshould be noted that although in FIG. 5 antenna 540 is shown to be usedto both transmit and receive signals, in general, each antenna may beused in half-duplex or in full-duplex without departing from theteachings of the present disclosure.

Typical wireless receivers have dynamic range of 60 dB. Ideally, theentire dynamic range is to be dedicated for capturing the backscatterreflections of the environment in which backscatter sensor is present.However, there are many spurious signals that prevent us from achievingthis goal.

In general, increasing the number of receiving antennas in thebackscatter sensor results in increased spatial resolution. In fact,wireless communications also benefits from larger number of receivingantennas. But, wireless antennas are typically of the order of 12.5 cmin length for 2.4 GHz operation. This large form factor limits thenumber of antennas that can exist in a wireless device. In order to savespace occupied by the antennas and to save the cost of antenna itself,wireless devices use the same antenna for dual purpose of transmissionand reception of signal. Almost all the existing wireless radios arehalf-duplex in nature, where they are either transmitting a signal orthey are receiving a signal but not both at the same time. Therefore,they can multiplex the use of a single antenna for either transmissionor reception.

It should be noted that the backscatter sensor should perform bothtransmission and reception of the signal simultaneously, or almostsimultaneously. The primary reason for this is that typical wirelesssignals have limited bandwidth which puts a limit on how sharp thetransmitted signal can be. In a typical indoor environment, thereflections from nearby objects arrive back at the receiver much earlierthan the interval that is needed to transmit the sharpest signal that isallowed by the limited bandwidth of the radio. Typically, the reflectedsignals are 100 billion times smaller than the transmitted signals,therefore, for proper operation of the backscatter sensor, we needability to transmit a strong signal and simultaneously listen to theweak reflections of the transmitted signal using the same antenna or adifferent antenna. This requirement is very similar to the one that isfaced by full-duplex radios, that transmit and receive simultaneouslyusing the same antenna.

In this disclosure, techniques that are described in the U.S. patentapplication No. 61/864,492 for full-duplex communication are used toboost the dynamic range of the backscatter sensor. In a full-duplexradio, as shown in FIG. 1, both the transmitter and the receiver chainsare connected to the same antenna via a circulator. Circulator is an RFdevice that tries to achieve unidirectional signal paths fromport#1-to-port#2 and from port#2-to-port#3 as shown by the two arrows inFIG. 1. However, parasitic path exists between port#1-to-port#3, whichcauses leakage of strong transmitted signal into the receive chain. Theleakage can saturate the receiver's ADC.

Additionally, some portion of the RF energy that is directed to theantenna from the transmitter, reflects back from the antenna instead ofradiating into the air due to the antenna imperfections. This RF energyalso leaks into the receiver from port#2-to-port#3 along with thereceived backscatter signal. Both of these spurious mechanisms oftransmit signal's leakage into the receiver is detrimental for operationof both the full-duplex radios and the backscatter sensor. However, theradiated RF energy from the antenna is also reflected back to thereceiver by reflectors in the nearby environment. This reflection isdetrimental for operation of the full-duplex radio, because the fullduplex radio is trying to receive a different signal, rather thanlistening to its own echo. However, backscatter sensor relies on thesereflections from the environment to extract interesting parametersregarding the reflectors.

Therefore, a full-duplex radio is designed to cancel all the traces oftransmitted RF signal that arrives at the receiver chain. Whereas, in abackscatter sensor, portion of the transmitted signal leaking viaparasitic paths in the circulator and via reflection due to antennaimperfection should to be canceled, while allowing the reflections fromthe environment to pass uninterrupted to the receiver. This differencein transmit signal cancellation engenders modification to the techniquesthat are developed for the full-duplex communication.

Additionally, applications that use backscatter sensor may selectivelywant to cancel certain reflections. For example, a motion tracingapplication built using the backscatter sensor may want to cancelbackscatter reflections coming from static objects, such as walls andfurniture's, which can easily be an order of magnitude stronger than theintended reflection from moving objects, such as human body. Such aselective cancellation in RF domain can have additional advantage ofimproving the accuracy of the intended reflections. Because the usefuldynamic range of the ADC can now represent the intended reflections frommoving objects with larger number of significant bits compared to whenthe strong unwanted reflections are present. They will also enable thebackscatter sensor to sense weaker reflections once the strongerreflections are removed despite limited dynamic range of the wirelessreceivers.

In one embodiment, a cancellation technique that is used by abackscatter sensor is disclosed. In an example full-duplex architecture,cancellation of the transmitted signal is done in RF domain by taking acopy of the transmitted signal and passing it through a tapped delayline RF filter, with fixed delay lines and variable attenuators. Thefilter response is dynamically adapted to match the composite channelthat the transmitted signal has passed through before reaching the inputof the receiver chain. This composite channel is collectivelyrepresented in frequency domain by H in FIG. 1. As mentioned before,this channel has two major components H=H_(a)+H_(e). The first part,H_(a), is the portion that includes the parasitic path via thecirculator and the reflection path coming from the antenna imperfection.And the second part, H_(e), is the portion that includes thecontributions from all the reflectors in the environment.

Channel H_(a) is fixed, since it corresponds to the response of passivecomponents of the radios (e.g., circulator and antenna, and the like).Channel H_(a) can be measured using vector network analyzer (VNA).Channel H_(e) is time varying, since it corresponds to changingreflections in the environment. In fact, in one embodiment, channelH_(e) is obtained by taking Fourier transform of the backscatter channelresponse described in Eqn. (5). Additionally, in one embodiment, ifvalue of the backscatter parameters corresponding to a certain reflectorin the environment is known, one can simply compute the correspondingfrequency domain contribution of the reflector in the channel H_(e) byusing Eqn. (5). This will come handy for applications that requirecancellations of specific reflections, and is one of the maindifferences between requirements of a full-duplex radio and thebackscatter sensor. From now on, H_(c) is used to denote a portion ofthe environmental channel H_(e) that a specific application wishes tocancel.

In one embodiment, an RF cancellation circuit may include multiple delaylines with variable attenuators for canceling the effect of transmittedsignal in the receiver input in a full-duplex radio. The delay lineshave fixed values and the attenuators have finite discrete values thatcan be selected electronically. The number of delay lines, their delayvalues, and the tuning range of these attenuators needs to be carefullydesigned in order for this filter to work in all environmentalconditions. Details of designing RF cancellation circuit is disclosed inthe U.S. patent application No. 61/864,492 for designing these RFfilters. The RF filter is tuned adaptively by a control algorithm byselecting appropriate attenuation values based on the cancellationrequirements.

In one embodiment, a first step in building the tuning algorithm is tocreate a dictionary frequency response matrix F_(i) whose columnsrepresent the pre-measured frequency response of the i^(th) delay lineand attenuator for a specific attenuation value. Such a dictionarymatrix is built for each of the delay line and attenuator combinationsof the RF cancellation filter. It should be noted that the practicalattenuators will have finite discrete value of attenuation. In addition,for each values of the attenuation, the filter also has a unique phaseshift and group delay. Thus, the number of columns in the dictionaryF_(i) equals the total number of unique values of attenuation in thei^(th) attenuator. Next, the following optimization problem is solved toobtain optimal values for all the attenuators in the RF cancellationfilter:

$\begin{matrix}\begin{matrix}\underset{\zeta_{i},{i = 1},\ldots,\Gamma}{minimize} & \left. ||{{\Sigma_{i}F_{i}\zeta_{i}} - \left( {H_{n} + H_{c}} \right)} \right.||^{2} \\{{subject}\mspace{14mu} {to}} & {{\zeta_{ij} \in \left\lbrack {1,0} \right\rbrack},} \\\; & {{{\Sigma_{j}\zeta_{ij}} = 1},{i = 1},\ldots,\Gamma,{j = 1},\ldots,\Lambda,}\end{matrix} & (8)\end{matrix}$

where the variable ζ_(ij) is a binary variable that indicates the j^(th)attenuator setting for the i^(th) attenuator of the RF cancellationfilter, Γ is the total number of delay lines in the filter, and A is thetotal number of attenuator values in each attenuators. This is acombinatorial optimization problem and is usually hard to solve.Therefore, in one embodiment, a relaxed version of this problem issolved that is convex. Thus, by changing the data input H_(c) to thisproblem, one can selectively cancel any specific reflectioncombinations. Therefore, backscatter sensor provides additionalmechanisms for higher-level applications to remove unwanted reflectionswhich can be a very powerful feature.

Progressive Interference Cancellation (PIC)

Embodiments of the present invention provide accurate backscattermeasurement for commodity radios by designing two novel techniques totackle the limitations of low dynamic range and finite bandwidthavailable in commodity radios. The algorithm starts by measuring theoverall channel response that the backscatter signal has gone through.This is the classic channel estimation that every receiver performs. Thedetails thereof will not be described herein for the sake of brevity.Hereinafter, the h[n] is assumed to be known, where y[n]=x[n]*h[n]. Notethat this h[n] is the composite overall response, but embodiments of thepresent invention break it down into the form where the individual τi,αi, θi values corresponding to each of the reflections that make up theoverall backscatter channel can be teased out.

By solving the equation (8), all the parameters of the backscatter maybe estimated. The above problem is non-linear because the sinc terms inthe optimization problem are non-linear. Further, the problem is notconvex. So embodiments of the present invention use a piece-wiseapproach to solve this problem. In other words, embodiments of thepresent invention focus on a small region in the variable space δτ, δα,δθ, approximate the sinc functions in that region by straight lines, andsolve the resulting convex optimization problem. The residual error iscompared to a threshold value and the process is repeated untilsuccessive iterations of the algorithm do not reduce the error anyfurther. The finite bandwidth and the discrete time nature of thechannel response are accordingly taken into consideration.

As an example of the application of the parameter estimation algorithmand selective backscatter cancellation technique, progressiveinterference cancellation (PIC) is disclosed. PIC is a powerful featureof backscatter sensor that can be used for range boosting. Whenbackscatter sensor is operating in an indoor environment, one of thechallenges is that some of the reflections in the backscatter may besignificantly stronger than the others. For example, if there is areflector 10 cm away from the transmitter and another reflector that is10 m away from the transmitter, the difference in signal strengths ofthese two components can be as high as 60 dB. Practical wireless radioshave a typical dynamic range of 60 dB, which would imply that the weakerreflection would get completely buried. Second, even if the weakerreflection is within the dynamic range, it may not be represented withmany bits. Hence, the farther reflector experiences a higherquantization noise. Therefore, even if the strong components can beestimated reasonably accurately, the weaker components may not beestimated properly, which may result in inaccurate backscatter parameterestimation.

According to an embodiment, accuracy can be improved if the strongestbackscatter components can be progressively removed to allow theestimation algorithm to operate on the remaining components, as shown inFIGS. 6A through 6D. By removing the stronger component, the receiver'sdynamic range can be used to sample the weaker components with higherresolution and thus prevent them from being washed out or distorted.Once such strongest components are estimated and removed, the next groupof components that are relatively weaker are estimated and removed. Thealgorithm recursively repeats until all components are estimated.

The challenge therefore is to selectively eliminate backscattercomponents by canceling them. Furthermore, the cancellation has to beimplemented in the analog domain before the receiver, otherwise thedynamic range will be limited. To implement this, the present inventionbuilds on prior work in self-interference cancellation and full duplexradio as described in U.S. application No. 61/864,492. In accordancewith the present invention, the backscatter components are selectivelyand progressively canceled in a controlled manner one by one.

To accomplish this, embodiments of the present invention use the sameestimation algorithm described above. The initial estimates are used tofind a coarse estimate of where the strong backscatter components arelocated. The analog cancellation circuit is then tuned to only cancelthese components by controlling the delay taps in the circuit tostraddle the delay of these strong components. After running theestimation algorithm on the remaining backscatter signal, the analogcancellation is further retuned to cancel the next strongest componentand the process is repeated. This cancellation block is referred to asProgressive self-Interference Cancellation (PIC). The analogcancellation may achieve a maximum of 70 dB of cancellation, soprogressively canceling backscatter cannot be used beyond a particularlevel. However, after such cancellation, assuming a receiver dynamicrange of at least 60 dB, the remaining components can be easilyestimated. Further, the same cancellation and estimation procedure canbe repeated in the digital domain by selectively canceling the residualbackscatter components.

The composite backscatter signal in the example shown in FIG. 6A isshown as having three strong reflections in addition to several smallerreflections. After the first step of progressive self-interferencecancellation, the first component is significantly reduced, while theother two strong components with higher delays are hardly affected, asshown in FIG. 6B. Next, the same algorithm is used to also cancel thesecond component and preserve only the third component. Notice also thatalthough the first and second components are close in amplitude anddelay (in fact the delay difference is within the sampling interval),embodiments of the present invention are able to selectively cancel thefirst and second reflections one after the other and leave the last onealone, as shown in FIG. 6C.

A subsequent analog cancellation step cancels the three strongestbackscatter components and that also hits the limit of what analogcancellation is capable of (e.g., 70 dB), as shown in FIG. 6D. However,the same progressive self-interference cancellation technique can beapplied to cancel the remaining backscatter components progressively viadigital cancellation. The digital cancellation step improves estimationaccuracy, but does not improve the dynamic range.

Usually the strength of the backscatter components are weaker for largervalues of the delay τ. Hence, according to embodiments of the presentinvention, the estimation algorithm will be run by partitioning therange for the variable τ into several consecutive non-overlappingwindows. In other words, the overall response of the backscatter signalis partitioned into a multitude of continuous adjacent linear regions,i.e., there is no gap between the approximate linear regions. In anembodiment, the size of the windows can be determined by estimating howfar apart the multi-paths are separated. In another embodiment, the sizeof the windows can be determined by the dynamic range of the receiver.For example, the size of the windows is determined such that no signalcomponents that lie within the window should exceed the dynamic range ofthe receiver. Therefore, the size of the windows varies dependent on theenvironment. If the response impulses of the backscatter signalcomponents are closely located and have a large difference in amplitude,then the algorithm will select a small window size. Conversely, if theresponse impulses of the backscatter signal components are widelylocated and have a small difference in amplitude, then the algorithm mayselect a large window size to cover several response impulses under thecondition that the amplitude difference among them is within the dynamicrange of the receiver.

After partitioning the overall impulse response into a multitude ofwindows, the algorithm begins by estimating parameters for thereflections in the first time window, then passes that information tothe PIC block. The backscatter estimator then removes the strongestcomponents within that window. After the cancellation of thesecomponents, the backscatter estimator advances the window one step intothe future and then estimates the parameters in that range and pass thatinformation to PIC for cancellation. This process can repeat multipletimes until all the backscatter components have been detected in theforward time direction. In an embodiment, the algorithm can set areasonably high value of τ beyond which the backscatter componentspresent are very weak; this maximum value depends on the application andthe environment.

FIG. 7 illustrates exemplary operations that may be performed by adevice for estimating backscatter signals, according to an embodiment ofthe present invention. In step 701, a radio frequency (RF) signal isoptionally transmitted to a plurality of objects (reflectors) togenerate a backscatter signal having a plurality of reflectioncomponents associated with the objects. The RF signal is transmitted ata carrier frequency. In another embodiment, the device receives RFsignals from external RF sources (e.g., that are transmitted from othersources in the environment such as existing Wi-Fi access points, and thelike). In general, the device can either transmit the RF signal itself,or receive backscatter reflections that are caused by transmissions fromother devices/sources without departing from the teachings of thepresent disclosure. The transmission may be through a single antennaand/or through a multiple input multiple output (MIMO) antenna array. Inan embodiment, the MIMO antenna array includes a plurality of antennasdisposed at half of the length of the carrier frequency.

In step 703, a receiver receives the backscatter signal through the MIMOantenna array. The receiver may have a limited bandwidth, a limiteddynamic range, as well as a limited dynamic sampling resolution. Thereceived backscatter signal has thus the shape of a sine function due tothe band-limited receiver. In step 705, the receiver partitions the sinefunction converted backscatter signal into a plurality of approximatelinear regions. In an embodiment, the size of the linear regions (alsoknown as window) can be determined in advance and depends from thedynamic range of the receiver. In another embodiment, the size of thelinear regions (windows) can be dynamically adjusted according to theenvironment, e.g., the number and the spatial separation of themulti-paths. The windows are consecutive non-overlapping, i.e., there isno gaps between the consecutive windows to detect all constituentbackscatter components.

In step 707, the receiver starts estimating parameters for reflectioncomponents of the objects that lie within the first window. Theparameters may include the attenuation, the delay time, and the angle ofarrival of the reflection components, the Doppler frequency and/or anyother parameters. The composite backscatter channel as seen in the m-threceiver in the MIMO antenna array can be modeled according to equation(5). The parameters of the constituent backscatter components can beestimated by solving the optimization expression (6).

In step 709, the estimated parameters are passed to an analogprogressive self-interference cancellation block for cancellation of thestrongest component within the window. In step 711, the process advancesto the next window and estimates parameters for one or more otherreflectors in this window until all windows are processed. In general,the progressive interference cancellation technique may be used ineither digital domain and/or analog domain.

The foregoing has described the principles, preferred embodiments, andmode of operation of the present invention. The present invention showshow by collecting, measuring and mining the natural backscatter thanhappens when radios transmit, objects can be detected and located withhigh accuracy. By implementing the progressive self-interferencecancellation and the self-interference cancellation circuitry andtechniques in the related application, commodity radios can be convertedto measuring devices to measure a backscatter signal and estimate itsproperties. Based on the estimated properties, a hidden object (e.g.,reflector) can be made visible. Embodiments of the present inventionprovide algorithms to enable commercially available transceivers to takeadvantage of the backscatter measurements to detect tumors, buriedbodies after an earthquake, or hidden weapons (e.g., knifes, guns), torecognize human gestures, to track human motion, to create indoorimaging system, to detect obstacles.

FIG. 8 is a simplified block diagram of an exemplary computer or dataprocessing system 800 in which portions of analog self-interferencecancellation circuit, such as controller 140 shown in FIG. 1, may beembodied. Computer system 800 is shown as including a monitor 810, acomputer 820, user output devices 830, user input devices 840,communications interface 850, and the like.

As shown in FIG. 8, computer 820 may include one or more processors orprocessing units 860 that communicates with a number of peripheraldevices via a bus subsystem 890. These peripheral devices may includeuser output devices 830, user input devices 840, communicationsinterface 850, and a storage subsystem, such as random access memory(RAM) 870 and non-volatile memory 880.

User input devices 830 include all possible types of devices andmechanisms for inputting information to computer system 820. These mayinclude a keyboard, a keypad, a touch screen incorporated into thedisplay, audio input devices such as voice recognition systems,microphones, and other types of input devices. User input devices 830typically allow a user to select objects, icons, text and the like thatappear on the monitor 810 via a command such as a click of a button orthe like. User output devices 840 include all possible types of devicesand mechanisms for outputting information from computer 820. These mayinclude a display (e.g., monitor 810), non-visual displays such as audiooutput devices, etc.

Communications interface 850 provides an interface to othercommunication networks and devices. Communications interface 850 mayserve as an interface for receiving data from and transmitting data toother systems. In various embodiments, computer system 800 may alsoinclude software that enables communications over a network.

RAM 870 and disk drive 880 are examples of tangible media configured tostore data including, for example, executable computer code, humanreadable code, or the like. Other types of tangible media include floppydisks, removable hard disks, semiconductor memories such as flashmemories, non-transitory read-only-memories (ROMS), battery-backedvolatile memories, and the like. RAM 870 and non-volatile memory 880 maybe configured to store the basic programming and data constructs thatprovide the functionality described above in accordance with embodimentsof the present invention. Software code modules and instructions thatprovide such functionality may be stored in RAM 870 and/or non-volatilememory 880. These software modules may be executed by processor(s) 860.RAM 870 and non-volatile memory 880 may also provide a repository forstoring data used in accordance with embodiments of the presentinvention.

RAM 870 and non-volatile memory 880 may include a number of memoriesincluding a main random access memory (RAM) for storage of instructionsand data during program execution and a read only memory (ROM) in whichfixed non-transitory instructions are stored. RAM 870 and non-volatilememory 880 may include a file storage subsystem providing persistent(non-volatile) storage for program and data files. RAM 870 andnon-volatile memory 880 may also include removable storage systems, suchas removable flash memory.

Bus subsystem 890 provides a mechanism for enabling the variouscomponents and subsystems of computer 820 communicate with each other asintended. Although bus subsystem 890 is shown schematically as a singlebus, alternative embodiments of the bus subsystem may utilize multiplebusses.

Various embodiments of the present invention may be implemented in theform of logic in software or hardware or a combination of both. Thelogic may be stored in a computer readable or machine-readablenon-transitory storage medium as a set of instructions adapted to directa processor of a computer system to perform the functions describedabove in accordance with embodiments of the present invention. Suchlogic may form part of a computer adapted to direct aninformation-processing device to perform the functions described above.

The data structures and code described herein may be partially or fullystored on a computer-readable storage medium and/or a hardware moduleand/or hardware apparatus. A computer-readable storage medium includes,but is not limited to, volatile memory, non-volatile memory, magneticand optical storage devices or other media, now known or laterdeveloped, that are capable of storing code and/or data. Various circuitblocks of the embodiments of the present invention described above maybe disposed in an application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), dedicated or shared processors,and/or other hardware modules or apparatuses now known or laterdeveloped.

The methods and processes described herein may be partially or fullyembodied as code and/or data stored in a computer-readable storagemedium or device, so that when a computer system reads and executes thecode and/or data, the computer system performs the associated methodsand processes. The methods and processes may also be partially or fullyembodied in hardware modules or apparatuses, so that when the hardwaremodules or apparatuses are activated, they perform the associatedmethods and processes. The methods and processes disclosed herein may beembodied using a combination of code, data, and hardware modules orapparatuses.

The above descriptions of embodiments of the present invention areillustrative and not limitative. For example, the various embodiments ofthe present inventions are not limited to the use antennas, transmitand/or receive chain elements, cancellation circuits, transceivers,digital to analog converters, analog to digital converters. Othermodifications and variations will be apparent to those skilled in theart and are intended to fall within the scope of the appended claims.

What is claimed is:
 1. A backscatter sensor, comprising: a receiver forreceiving a composite signal comprising one or more reflections of atransmitted signal, each reflection being reflected by one of aplurality of objects; and a processor configured to: estimate at least afirst backscatter component of the composite signal using a progressiveinterference cancellation technique, wherein the first backscattercomponent of the composite signal corresponds to a reflection of thetransmitted signal from a first object.
 2. The backscatter sensor ofclaim 1, wherein the receiver further comprises: a plurality of receiveantennas, each receive antenna being coupled to a receive chain, whereineach receive chain receives a modified copy of the composite signal. 3.The backscatter sensor of claim 1, further comprising: a transmitter fortransmitting the transmitted signal, and wherein the processor furtherreceives a sample of the transmitted signal.
 4. The backscatter sensorof claim 3, wherein the transmitter comprises one or more transmitters,each transmitter transmitting a signal.
 5. The backscatter sensor ofclaim 1, wherein the processor is further configured to estimate atleast one of an amplitude of the first backscatter component, a phase ofthe first backscatter component and a time delay between transmission ofthe transmitted signal and reception of the first backscatter component.6. The backscatter sensor of claim 1, wherein the processor is furtherconfigured to: remove the estimated first backscatter component from thecomposite signal to generate a second signal; and estimate a secondbackscatter component using the second signal, the second backscattercomponent corresponding to a reflection of the transmitted signal from asecond object.
 7. The backscatter sensor of claim 1, wherein the firstobject is the closest object to the receiver among the plurality ofobjects.
 8. The backscatter sensor of claim 1, wherein the processor isfurther configured to estimate one or more parameters associated with atleast the first backscatter component using a linear optimizationtechnique.
 9. The backscatter sensor of claim 8, wherein the processoris further configured to estimate the one or more parameters inaccordance with the following expression: $\begin{matrix}{{minimize}\;} & {\left. {\Sigma_{m}\Sigma_{n}}||{{h_{m}\lbrack n\rbrack} - {{\overset{\_}{h}}_{m}\lbrack n\rbrack}} \right.||^{2}\mspace{79mu}} \\{{subject}\mspace{14mu} {to}} & {{{\tau_{k} \geq 0},{\alpha_{k} \leq 1},}} \\\; & {{{\theta_{k} \in \left\lbrack {\frac{- \pi}{2},\frac{\pi}{2}} \right\rbrack},{\mu_{k} \in \left\lbrack {{- \pi},\pi} \right\rbrack},}\mspace{34mu}} \\\; & {{k = \left\{ {1,\ldots,L} \right\}},{n = \left\{ {{- N},\ldots,N} \right\}},} \\\; & {{m = \left\{ {1,\ldots,M} \right\}}}\end{matrix}$ wherein {tilde over (h)} represents an estimated channelresponse, M represents number of antennas, L represents number ofreflected backscatter components, α_(k) represents an attenuationundergone by the k^(th) backscatter component, ν_(k) represents a phaserotation seen by the k^(th) backscatter component, τ_(k) represents adelay between the k^(th) backscatter component and a correspondingtransmitted signal, and Θ_(k) represents an angle of arrival of k^(th)backscatter component.
 10. The backscatter sensor of claim 1, whereinthe receiver is further configured to receive the composite signalduring a plurality of consecutive non-overlapping time windows, and theprocessor is further configured to: estimate one or more parameters ofthe first backscatter component based at least on a first portion of thecomposite signal received in a first window; and iteratively estimateparameters of a next backscatter component based on a second portion ofthe composite signal received in a next consecutive window.
 11. Thebackscatter sensor of claim 10, wherein the consecutive non-overlappingwindows have a variable size.
 12. The backscatter sensor of claim 1,wherein the processor is further configured to estimate the at least thefirst backscatter component using a Sequential Convex Programming (SCP)algorithm.
 13. The backscatter sensor of claim 12, wherein the processoris further configured to generate an initial estimate for the SCPalgorithm using a continuous basis pursuit (CBP) algorithm.
 14. Thebackscatter sensor of claim 1, wherein the processor is furtherconfigured to estimate the at least the first backscatter componentusing a continuous basis pursuit (CBP) algorithm.
 15. The backscattersensor of claim 1, wherein the processor is further configured toestimate a Doppler frequency corresponding to each object.
 16. A methodfor sensing backscatter signals, comprising: receiving a compositesignal comprising one or more reflections of a transmitted signal, eachreflection being reflected by one of a plurality of objects; andestimating at least a first backscatter component of the compositesignal using a progressive interference cancellation technique, whereinthe first backscatter component of the composite signal corresponds to areflection of the transmitted signal from a first object.
 17. The methodof claim 16, further comprising: receiving a plurality of compositesignals by a plurality of receive antennas, each receive antenna beingcoupled to a receive chain.
 18. The method of claim 16, furthercomprising: transmitting one or more signals using one or more transmitantennas; and estimating one or more backscatter componentscorresponding to each transmitted signal.
 19. The method of claim 16,wherein estimating at least a first backscatter component comprisesestimating at least one of an amplitude of the first backscattercomponent, a phase of the backscatter component, and a time delaybetween transmission of the transmitted signal and reception of thefirst backscatter component, time of flight of the first backscattercomponent.
 20. The method of claim 16, further comprising: removing theestimated first component from the composite signal to generate a secondsignal; and estimating a second backscatter component using the secondsignal, the second backscatter component corresponding to a reflectionof the transmitted signal from a second object.
 21. The method of claim16, wherein the first object is the closest object to the receiver amongthe plurality of objects.
 22. The method of claim 16, furthercomprising: estimating one or more parameters associated with at leastthe first backscatter component using a linear optimization technique.23. The method of claim 22, wherein the linear optimization techniquecomprises solving the following expression: $\begin{matrix}{{minimize}\;} & {\left. {\Sigma_{m}\Sigma_{n}}||{{h_{m}\lbrack n\rbrack} - {{\overset{\_}{h}}_{m}\lbrack n\rbrack}} \right.||^{2}\mspace{79mu}} \\{{subject}\mspace{14mu} {to}} & {{{\tau_{k} \geq 0},{\alpha_{k} \leq 1},}} \\\; & {{{\theta_{k} \in \left\lbrack {\frac{- \pi}{2},\frac{\pi}{2}} \right\rbrack},{\mu_{k} \in \left\lbrack {{- \pi},\pi} \right\rbrack},}\mspace{34mu}} \\\; & {{k = \left\{ {1,\ldots,L} \right\}},{n = \left\{ {{- N},\ldots,N} \right\}},} \\\; & {{m = \left\{ {1,\ldots,M} \right\}}}\end{matrix}$ wherein {tilde over (h)} represents an estimated channelresponse, M represents number of antennas, L represents number ofreflected backscatter components, α_(k) represents an attenuationundergone by the k^(th) backscatter component, ν_(k) represents a phaserotation seen by the k^(th) backscatter component, τ_(k) represents adelay between the k^(th) backscatter component and a correspondingtransmitted signal, and Θ_(k) represents an angle of arrival of k^(th)backscatter component.
 24. The method of claim 16, further comprising:receiving the composite signal during a plurality of consecutivenon-overlapping time windows; and estimating one or more parameters ofthe first backscatter component based at least on a first portion of thecomposite signal received in a first window; and iteratively estimatingparameters of a next backscatter component based on a second portion ofthe composite signal received in a next consecutive window.
 25. Themethod of claim 24, wherein the consecutive time windows correspond todifferent sizes.
 26. The method of claim 16, further comprising:estimating the at least the first backscatter component using aSequential Convex Programming (SCP) algorithm.
 27. The method of claim26, further comprising: generating an initial estimate for the SCPalgorithm using a continuous basis pursuit (CBP) algorithm.
 28. Themethod of claim 16, further comprising: estimating the at least thefirst backscatter component using a continuous basis pursuit (CBP)algorithm.
 29. The method of claim 16, further comprising: estimating aDoppler frequency corresponding to each object.